Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with an embodiment, described herein is a system and method for providing query acceleration with a computing environment such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. A middle layer is provided as a long-term table data storage format; and one more acceleration formats, or acceleration tables, can be periodically regenerated from the middle layer, wherein a determination can be made as to whether an accelerated table exists for a dataset table, and if so, then the accelerated table is used to process the query.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more segments within a data set, associated with a target attribute value, based on, for example, the use of a classification and regression tree and a combination of different driving factors, or same driving factors with different values. Information describing segments associated with the data set can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: A computer implemented algorithm performs introspection of an uploaded denormalized table and identifies candidate fact and dimension tables. The cardinality values of columns in a candidate dimension table are analyzed to identify simple/complex primary key candidates. Unused columns are further analyzed for assignment to candidate fact and/or dimension tables.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more segments within a data set, associated with a target attribute value, based on, for example, the use of a classification and regression tree and a combination of different driving factors, or same driving factors with different values. Information describing segments associated with the data set can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: Techniques for providing predictive metrics relating to employment positions are provided. A method may include receiving, by a computing device, data relating to a plurality of employment positions, wherein the data is received from a plurality of customers. The computing device may aggregate the data received from the plurality of customers and may determine statistics using the aggregated data, which are based on each of the plurality of employment positions. The computing device may generate one or more predictive metrics relating to the plurality of employment positions using one or more of the statistics.

Abstract: Techniques for providing predictive metrics relating to employment positions are provided. A method may include receiving, by a computing device, data relating to a plurality of employment positions, wherein the data is received from a plurality of customers. The computing device may aggregate the data received from the plurality of customers and may determine statistics using the aggregated data, which are based on each of the plurality of employment positions. The computing device may generate one or more predictive metrics relating to the plurality of employment positions using one or more of the statistics.

Abstract: Techniques for providing predictive metrics relating to employment positions are provided. A method may include receiving, by a computing device, data relating to a plurality of employment positions, wherein the data is received from a plurality of customers. The computing device may aggregate the data received from the plurality of customers and may determine statistics using the aggregated data, which are based on each of the plurality of employment positions. The computing device may generate one or more predictive metrics relating to the plurality of employment positions using one or more of the statistics.

Abstract: A computer implemented algorithm performs introspection of an uploaded denormalized table and identifies candidate fact and dimension tables. The cardinality values of columns in a candidate dimension table are analyzed to identify simple/complex primary key candidates. Unused columns are further analyzed for assignment to candidate fact and/or dimension tables.

Abstract: Embodiments of the invention include a method of implementing a touch screen optimized pivot table. The method includes providing a view of a hierarchal table on a touch screen. The hierarchal table includes hierarchal levels and each of hierarchal levels includes corresponding data. The method further includes mapping each of the hierarchal levels to each of a corresponding region on the touch screen. Each region's view displays the corresponding data for the mapped hierarchal level. The method further includes displaying on the touch screen the corresponding data for each of the hierarchal levels in each of the mapped regions, receiving at the touch screen input to manipulate at least one of the regions on the touch screen, and in response to the received input, causing each subordinate region to the at least one manipulated region to update the corresponding data displayed in each of the subordinate regions.

Abstract: Techniques for providing predictive metrics relating to employment positions are provided. A method may include receiving, by a computing device, data relating to a plurality of employment positions, wherein the data is received from a plurality of customers. The computing device may aggregate the data received from the plurality of customers and may determine statistics using the aggregated data, which are based on each of the plurality of employment positions. The computing device may generate one or more predictive metrics relating to the plurality of employment positions using one or more of the statistics.

Abstract: Embodiments of the invention include a method of implementing a touch screen optimized pivot table. The method includes providing a view of a hierarchal table on a touch screen. The hierarchal table includes hierarchal levels and each of hierarchal levels includes corresponding data. The method further includes mapping each of the hierarchal levels to each of a corresponding region on the touch screen. Each region's view displays the corresponding data for the mapped hierarchal level. The method further includes displaying on the touch screen the corresponding data for each of the hierarchal levels in each of the mapped regions, receiving at the touch screen input to manipulate at least one of the regions on the touch screen, and in response to the received input, causing each subordinate region to the at least one manipulated region to update the corresponding data displayed in each of the subordinate regions.

Abstract: Techniques to provide dynamic multi-lingual support for software applications such as analytics, which typically rely on two different types of data, underlying data and metadata, to generate reports. The metadata defines the underlying data and a presentation layer displays the reports, which may be generated using presentation and metadata layer objects. Each such object may be defined by a language independent representation, which may in turn be associated with multiple display or language dependent representations (i.e., translations or translated strings) for multiple supported languages. One of the languages may be selected for use in a given session, and the display representations for the selected language are then bound to the language independent representations. The binding may be dynamically performed and is session-specific.